Abstract
Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for underlying surface. However, existing methods perform upsampling on a single patch, ignoring the coherence and relation of the entire surface, thus limiting the upsampled capability. Also, they mainly focus on a clean input, thus the performance is severely compromised when handling scenarios with extra noises. In this paper, we present a novel method for more effective point cloud upsampling, achieving a more robust and improved performance. To this end, we incorporate two thorough considerations. i) Instead of upsampling each small patch independently as previous works, we take adjacent patches as input and introduce a Patch Correlation Unit to explore the shape correspondence between them for effective upsampling. ii)We propose a Position Correction Unit to mitigate the effects of outliers and noisy points. It contains a distance-aware encoder to dynamically adjust the generated points to be close to the underlying surface. Extensive experiments demonstrate that our proposed method surpasses previous upsampling methods on both clean and noisy inputs.
Abstract (translated)
URL
https://arxiv.org/abs/2109.09337
